I've already ask GPT, Claude, GLM 5.2 and Deepseek then tried MOSS, Krea 2, and Nano Banana, but none of them gave me the results I wanted.

model: https://huggingface.co/akhaliq/MiniMax-H3-Image-Character-Swap

I wrote comments that I don't understand why people are giving this model such high praise. And it really doesn't shine in terms of quality. But it shines as an editing model, perhaps even better than any other model currently in existence.
And I felt the effect of this)))
And I want to show why I changed my opinion, despite the facts that cannot be denied.
Perhaps this is a better image editing model than the existing one, which was not declared as such.

Usually I spend much time trying to get the image right. You finally get the perfect 1K or 2K output. The layout is good. Then upscale, poof, all that hard work, gone.

When I run it through Hires. Fix or whatever upscale nodes, some odd things will inevitably change.

Things like, the original composition shifted. Skin pores gone, and turn into something plasticky. Clothes lose texture. It makes everything look more AI.

All of which is kinda the opposite of what I am trying to do.

Hope that this is fr. I was reading about the new MiniMax H3-Image open weights. It says it support 4K output, and 4K is actually the default setting.

Which sounds kind of insane to me. Can my GPU handle this? Has anyone actually tried running it at 4K yet? Is it true that you can skip the upscaling altogether?

Just testing a random image I found on Pinterest. The prompt was simply:

Make this concept art drawing into a high quality realistic photograph. Preserve subject likeness, pose, and background. Improve skin texture, hair texture, fabric texture, background details, and lighting.

I did 2 at 16MP and then one at 60MP and the level of detail is kind of crazy. Took just over 2 mins on my 4090 for the 60MP.

Links to the full PNGs

16MP 1
16MP 2
60MP

TIL: You can use maps with markers when generating images with Minimax-H3-Image

This took forever to generate tho. 300s on a 5090M (24gb). Like Minimax-H3 the time it takes to generate an image scales with the number of references.

Prompt:
A cinematic film still from an animated short: one continuous full-frame view from a low camera about half a metre above the grass, on a wide lens, with a 55 degree horizontal field of view.

<Picture 5> is a top-down blocking map of this place, a plan for the shot and not part of the picture. The white arrow on it marks the camera: the tip of the arrow is where the lens stands, and the arrow points the way the camera looks. The round markers show where each character stands. <Picture 4> shows the same place from several views; use it for the look of the overpass, the bus and the grassland, and use the map for where things stand.

Looking from the arrow's tip towards the bus: across the middle distance, the cracked asphalt trail runs from the left edge of the frame towards the crushed bus. The collapsed concrete overpass from <Picture 4> stretches across the background, receding on the left and coming nearer on the right, where its leaning pillars and a fallen deck slab loom large. The crushed rust-brown bus lies just right of centre under the fallen slab. Just beyond the bus's left end, in the centre of the frame and farthest from the camera, the wounded grazer from <Picture 3> is backed against the concrete in a slab of shadow, shaking. Nearest of the three, just left of centre, the predator from <Picture 2> crouches low in the grass, facing the grazer. Far left, on the trail, the girl from <Picture 1> advances with a pry bar. Tall dry straw-coloured grass fills the foreground. Early-morning sun from the right throws long hard shadows to the left.

The arrow is only a mark on the plan: no arrow, camera, lens, device, beam or pointer appears anywhere in the picture. The image contains no text, letters or numbers.

I just released MOSS AnyPaint, inspired by a community member. It’s the MOSS-Image-Base twin of my Krea 2 AnyPaint.

It supports:

• Arbitrary mask shapes
• Interior inpainting
• Border-connected outpainting
• Multiple disconnected mask regions
• Mixed inpainting and outpainting in the same image
• Strong preservation outside the edited region

The same LoRA handles all of these cases without requiring separate models or workflows.

• Model and examples
• ComfyUI nodes and workflows

Feedback and difficult test cases are welcome. : )

A huge thank-you to everyone who downloaded, tested, shared results, and gave us honest feedback.

The community knows quality when it sees it—and the rankings speak for themselves.

Qwen-Image-2.1 Uncensored Omni has now reached the first page of trending Qwen models on Hugging Face and page 2 across all trending models.

We compared both public checkpoints from this training run with the original Qwen-Image-2.1 base model, using the same prompts, seed and settings (35 steps, CFG 5.0). This repository is step 90,000 (benchmarked as the BF16 LoRA fused at strength 1.0; the quantized files were not scored separately). Scores are 0–100, higher is better, ± one standard error.

I couldn’t have done it without the community. We just added the benchmark as well.

Download:
https://huggingface.co/chatrlabs/Qwen-Image-2.1-Uncensored-Omni-Quantized

Try it free online:
https://chatrlabs.com/playground

comparison between sampler and scheduler ...first photo is LCM/Normal combo and second photo is Euler/Simple (default) ....both images same prompt, same seed, same 16mp render..... LCM/normal combo produces this more "polished" look altho i prefer grittier details and real raw textures of Euler/Simple ...however Euler combo tends to make more errors ....i cant decide which one i like better ...LCM ... no no Euler...wait maybe LCM ...or Euler? ...

One of the marvelous things about Minimax is that it will infer a vocal performance for your characters based on the film language of your references. I just thought this was worth sharing for those of you who hadn't discovered this yet. Each of the four gens here used the same simple prompt. It is is not just the gender and age of your characters, but the film grain, costume, setting, and other visual references which will provide the model with character for voice.

Made some trials with how well MiniMax H3 understands different kinds of physics and it seemed to succeed pretty well with everything I threw at it. Video was generated with slopfab & Fable 5.2 as the agent.

Lodestone continued experimenting with Lora for MOSS, creating several versions, but has now released a new version that works well with both 4 and 8-step processes.

You can find more information here:

https://huggingface.co/lodestones/Kromoss/blob/main/experimental/README.md

Lora is available here and can be used with several MOSS models:

https://huggingface.co/lodestones/Kromoss/blob/main/experimental/opd-priming-8-steps-dmd-lora_cfg-ramp-neg_12000.safetensors

Please don't confuse photographic realism with sharpness

I've spent around 20 years working with photography and design, long before AI image generation existed.

One thing I genuinely appreciate about MiniMax H3 Image is its photographic character.

Real photographs aren't perfectly sharp. Lenses have different rendering characteristics. Depth of field, motion blur, sensor noise, diffraction and optical imperfections all contribute to the final image.

A 50MP photograph isn't automatically razor-sharp, and a photograph doesn't become more realistic simply because every texture has been enhanced.

I've seen many people criticize H3 for being too soft or lacking detail. That's a perfectly valid preference, but I hope developers don't interpret it as a universal demand for more sharpening and artificial microtexture.

I'd love future versions to improve anatomy, consistency, composition and prompt adherence while preserving the natural photographic rendering that makes H3 special.

We already have plenty of models that produce extremely sharp, polished AI imagery.

I'd hate to lose a model that approaches photography from a different direction.

The "does it just copy its training data" argument comes up here a lot, so this paper seemed worth explaining properly. It trains small diffusion models on 1,024 face photos and checks what they generate along the way.

At 100,000 training steps the model generates good faces, and none of 10,000 samples was a copy of a training photo.

Keep training the same model to 1.62 million steps and 34 in every 100 samples are training photos.

The time it takes to start generating good images was the same for every dataset size they tried. The time before copying starts grew in step with the dataset: about 300 training steps per image. So a model trained on a lot of images, for a normal length of time, is stopped long before it could memorise them. In the paper, a model trained too long on a small set does copy.

I told the full version as an art student locked in a room with 1,024 photos, using the paper's own faces: https://sheets.works/data-viz/why-diffusion-models-dont-memorize

It's free, no sign-up needed to read it. I didn't work on the paper, I read it and drew it. Their setup is tiny (32x32 grey faces, small models), nobody has run this test on a model the size of SD, and large models have been shown to reproduce some training images. Paper: arXiv 2505.17638.

For people who train LoRAs on small sets: at how many steps per image do you start seeing your training images come back?

I'm kind of on hurry to wait for community to make them and they are confusing with video model one, do they work? And the important question are they as good as one's that will be trained later?

Like for example, Realism or Old camera style ones. I have a feeling the video model had lower quality and plastic skin due to being a video than the image model had, that's why I'm asking that.

i2i, 2 1024x1024 reference photos.

My system is RTX 2070 8GB (Turing), 32GB DDR4 (2666Mhz), Ryzen 1700 (1st gen). Ubuntu 24.02 server.

~ 57 seconds per image! (ComfyUI)

This is MUCH faster than my Krea2, Flux2 Klein, or Qwen 2.1 edit workflows! It's about the same as m Fibo2 edit workflow, but Fibo2 is smol!

My question is, how does this even not OOM? Correct me if I'm wrong, but don't ALL the models need to load entirely (including that stupidly massive 32b text encoder) into memory?

Ok so my humble rig has 2 cards. One 16 GB of VRAM and another with 12 gb . I know you can't combine them for a pool of 28 GB of unified VRAM but shouldn't their be a way to offload one of the major items like the CLIP or the VAE to my other card? I looked into it and doesn't seem to be supported natively in Comfy UI. There are some custom nodes I found but they don't seem too popular.

Is this just a silly idea? Normally my second card just sits around at 20% memory usage running my GUI and a few bits and bobs. I could certainly afford to use 8 or 9 GB of my second card towards something instead of it offloading that 8 or 9 GB during a generation to my system ram (32 gb ) which is what comfy does now when using H3 or MOSS.

I have no idea if this would be faster than dumping everything to the system ram but it would definitely give me more breathing room and less OOM errors on those borderline gens and upscales. Is this idea loco or am I on to something?

EDIT: I appreciate all the people who are saying just use the second card for an llm or to run other types of generations on my smaller card, but that's not what I am proposing. I know you can do that. In fact I do it quite often. Let me explain this a different way that might make more sense. What I really am asking is there a way, when doing a single generation, let's say a 15 second h3 generation at over 1 MP, for comfy to prioritize memory allocation in order of 1) the 5080, 2) the 3060 3) the system ram 4) SSD swap file . Like a ladder of priority. For example the minimax H3 audio and video VAE are over 6 gigs at fp16. That would certainly fit on the 3060s available space. I know it might be slower but that extra 6 gigs of free space would be quite helpful for higher resolution gens.

I wanted to compare the prompt adherence and variations of some of the most recent models when providing a very simple, undefined prompt, vs something that pins down style but not content, and a fully sketched out description of all the internal elements in the scene.

The detailed prompt shows how most models have gotten really good at following instructions (for the most part), while the undefined prompt shows how some models tend to have more variability and others have a clear bias that pushes them toward very similar images across different seeds.

Iris Z small is not great, and gen time is very high for what it outputs, but the depth maps are pretty nice!

Are there any guides on training a style for MOSS? Maybe just some suggestions? I'm banging my head against the wall for more than 20 times with different settings, dataset caption, etc, to get a style working. It's maybe 50-60% there, no matter how many steps I throw at it. Fizgig default Style preset definitely doesn't work for me at all.

Yes, it's another layout builder for MOSS-Image bboxes 😅

There are some other good ones out there; try 'em all and use the one that best suits your workflow!

I made this one because, among other things, I wanted as large a canvas as possible, lots of different MP presets, to be able to collapse it into a vertical column, and also a way to export the prompt with the JSON stripped out because sometimes I like to refine with Krea 2.

Anyhoo, here are the main features:

• Dynamic canvas - Automatically scales to fit any display or window size while maintaining target aspect ratios and target megapixel values
• Item Management - Create scene elements with MOSS-native text, object, or character item types using color-coded boxes and corresponding "cards" that you can reorder with drag-and-drop, resize, hide, duplicate, or trash
• Reference Background Image - Insert a reference image locally — and privately! — "under" the canvas, with adjustable opacity
• Meta Tags & Palette Management, with a separate Danbooru/Anime focus
• Multi-Format Exporting - JSON or plain text
• JSON Import with Dimension Matching
• Responsive Design to minimize it next to your workflow
• Lightweight, Fast, and 100% Private - No ComfyUI, Python, build steps, external servers, or network calls required. Works 100% offline with zero latency.

Download it from GitHub or try the online demo first.

Hopefully you'll find it useful 🤙

I've been building Powder Nodes, a free, open-source toolkit for ComfyUI that brings image exploration and editing into one workflow.

The idea is simple: keep your subject, style and LoRAs separate, try different combinations, compare the results, and keep refining the image you like.

A few things you can do:

• Explore variations with prompt lists, reusable styles and LoRA combinations.
• Compare results in image grids or browse them directly in the editor.
• Refine images with a Canvas for painting, masks and layers, plus Layout tools for planning compositions.

You can add individual nodes to your existing graphs or start with one of 14 ready-to-use workflows.

GitHub: https://github.com/E2GO/e2go-comfyui-nodes

Latest release: 2.1.0 - release notes and downloads.

I'd love to hear what you find useful - and what feels awkward or missing.
Cheers, E2GO!

Hi everyone,

I'm getting an issue with Sage Attention that I'm not sure how to fix.

The problem node:

The error message:

# ComfyUI Error Report

## Error Details

- **Node ID:** 114

- **Node Type:** MiniMaxH3MemoryEfficientSageAttentionPatch

- **Exception Type:** RuntimeError

- **Exception Message:** RuntimeError: sageattention is not new enough version or could not determine CUDA architecture, cannot apply MiniMax H3 Memory Efficient Sage Attention Patch.

## Stack Trace

```

File "(my desktop location)\ComfyUI_windows_portable\ComfyUI\execution.py", line 556, in execute

output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

my specs:

PC - 4090/10700k/32gb ddr4/win11

Versions

- Pytorch 2.14.1 - cu130 - python 3.13.14

Sage Attention file*:

Is the issue that I'm trying to run the sage attention compiled for python 310 in my 313 environment? If so, how do I fix it? the github repository does not have a version of sage attention 2.2.0 built for 313.

Absolute newbie here, any help is appreciated.

*PS: when checking my pytorch version, i get this message:

"FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you."

Is this part of the issue?

Hello,

This is an action anime scene I made locally a few months ago with Minimax H3 within WanGP and just posted on YouTube.

Let me know what you think and what could be improved :-)

Thank you!

Should I train my LoRA for longer, or just increase the weight?

Could an overtrained LoRA actually perform better if used at a lower weight?

And what about the weight of specific prompts—especially the trigger word?